As organizations start to use data-intensive cluster computing systems like Hadoop and
Dryad for more applications, there is a growing need to share clusters between users …

During the recent years, workflows have emerged as an important abstraction for
collaborative research and managing complex large-scale distributed data analytics …

In scientific cloud workflows, large amounts of application data need to be stored in
distributed data centres. To effectively store these data, a data manager must intelligently …

Sharing a MapReduce cluster between users is attractive because it enables statistical
multiplexing (lowering costs) and allows users to share a common large data set. However …

Computationally intensive tasks of IoT applications can be offloaded to powerful devices in
the edge. Code offloading reduces energy consumption and increases performance …

Scientific workflows play an important role in today's science. Many disciplines rely on
workflow technologies to orchestrate the execution of thousands of computational tasks …

A constant rebalanced portfolio is an investment strategy that keeps the same distribution of
wealth among a set of stocks from day to day. There has been much work on Cover's …

High quality of security service is increasingly critical for Cloud workflow applications.
However, existing scheduling strategies for Cloud systems disregard security requirements …

Cloud environments offer low-cost computing resources as a subscription-based service.
These resources are elastically scalable and dynamically provisioned. Furthermore, cloud …

Cloud computing is a promising distributed computing platform for big data applications, eg,
scientific applications, since excessive resources can be obtained from cloud services for …